{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8f2a117-6e96-46e5-a5b8-c8585686b4f9",
   "metadata": {},
   "source": [
    "# 邂逅Python\r\n",
    "\r\n",
    "在这个课程中，我们将一起探索Python这门强大的编程语言，从它的历史发展，为何选择学习它，如何安装以及如何使用它的开发环境。让我们开始这段旅程，邂逅Python的魅力。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a58b2f8-c03a-4ca9-b01b-ac48ba05fb33",
   "metadata": {},
   "source": [
    "## 第一部分：Python的发展史\r\n",
    "\r\n",
    "### 简介\r\n",
    "- Python是由Guido van Rossum在圣诞节期间为了打发时间开始编写的一个编程语言项目。\r\n",
    "- 它的名字来源于BBC的喜剧电视剧《蒙提·派森的飞行马戏团》。\r\n",
    "- Python的设计哲学强调代码的可读性和简洁的语法，特别是使用空格缩进来区分代码块。\r\n",
    "\r\n",
    "### 发展里程碑\r\n",
    "- **1991年**：Python 0.9.0发布，包括异常处理、函数定义和类的基本形式。\r\n",
    "- **2000年**：Python 2.0发布，增加了垃圾回收和完全的Unicode支持。\r\n",
    "- **2008年**：Python 3.0发布，这是一个不向后兼容的版本，意味着旧代\n",
    "### Python特点\r\n",
    "- **简洁的语法**：使得Python特别容易上手，被广泛用于编程教学。\r\n",
    "- **丰富的库和框架**：Python有一个庞大的标准库，几乎可以处理所有常见的编程任务。\r\n",
    "- **跨平台**：Python可以运行在多种操作系统上，包括Windows、Mac OS X和Linux。码需要修改才能运行。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dff66a9e-fdcb-4cc8-a1ae-0efb1851c5cf",
   "metadata": {},
   "source": [
    "## 第二部分：为什么学习Python及其应用\n",
    "\n",
    "### 易于学习\n",
    "- Python的设计哲学是\"优雅\"、\"明确\"、\"简单\"。\n",
    "- 对初学者友好，是许多大学和教育机构推荐的第一门编程语言。\n",
    "\n",
    "### 广泛应用\n",
    "- **数据分析与数据科学**：利用Pandas进行数据清洗和分析，NumPy进行数值计算。\n",
    "- **机器学习与人工智能**：使用Scikit-learn进行机器学习，TensorFlow和PyTorch进行深度学习。\n",
    "- **Web开发**：Django和Flask框架可以快速开发复杂的Web应用。\n",
    "- **自动化脚本**：简化日常任务，如文件管理、数据爬取。\n",
    "\n",
    "### 高需求\n",
    "- Python开发者的需求持续增长，尤其是在数据科学和机器学习领域。\n",
    "- 学习Python可以为学生打开众多职业道路的大门。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd85c4e0-f283-41e0-b6c4-1ce797c09f3b",
   "metadata": {},
   "source": [
    "## 第三部分：Python的安装\r\n",
    "\r\n",
    "### 直接安装Python\r\n",
    "1. 访问[Python官方网站](https://www.python.org/)，下载最新版的Python。\r\n",
    "2. 选择适合你的操作系统的安装程序。\r\n",
    "3. 安装时确保选中了“Add Python to PATH”选项，这样可以在命令行中直接运行Python。\r\n",
    "\r\n",
    "### 使用Anaconda安装Python\r\n",
    "1. 访问[Anaconda官方网站](https://www.anaconda.com/)，下载Anaconda。\r\n",
    "2. 安装Anaconda，它会自动安装Python和一系列科学计算相关的库。\r\n",
    "3. 利用Anaconda的环境管理器，可以方便地管理不同项目所需的不同版本的库和Python版本。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4b2f72-b757-4e8f-b411-4bfc3598c196",
   "metadata": {},
   "source": [
    "## 第四部分：常用的IDE介绍\r\n",
    "\r\n",
    "### PyCharm\r\n",
    "- JetBrains出品，功能强大的Python IDE，适合专业的软件开发。\r\n",
    "- 提供代码分析、图形化的调试器、一个集成的测试器、Git集成等高级功能。\r\n",
    "\r\n",
    "### Visual Studio Code (VSCode)\r\n",
    "- 微软开发的免费、开源的编辑器。\r\n",
    "- 支持多种语言，通过安装Python扩展，可以获得类似IDE的功能。\r\n",
    "- 社区强大，插件众多，可以根据需要定制开发环境。\r\n",
    "\r\n",
    "### Jupyter Notebook\r\n",
    "- 适合于数据分析、机器学习、科学计算和教育。\r\n",
    "- 允许创建和共享包含实时代码、方程、可视化和文本的文档。\r\n",
    "- 通过Anaconda安装，可以直接从Anaconda Navigator启动Jupyter Notebook。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "172b52b9-7089-4139-af7b-2c5855516ee1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.10.13\n"
     ]
    }
   ],
   "source": [
    "!python -V#查看python版本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c56ad0d-c627-429e-8dd2-bce6cb129336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    }
   ],
   "source": [
    "#用Anaconda 安装jupyter\n",
    "!conda install jupyter notebook --update-deps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d058cd01-3705-4478-86e3-752ef16925b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, world!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello, world!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "568c4f43-09bf-4e87-80d7-60f9496d7469",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = input(\"你是谁？\")\n",
    "print(\"你好，\" + name + \"！欢迎来到Python的第一节课。\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4bf6a4a9-0ebe-4ac0-9423-ab9131cd001d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: numpy in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (1.26.4)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (10.2.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0fc934b8-84e2-4479-8fc8-5742212cbc43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "==> WARNING: A newer version of conda exists. <==\n",
      "  current version: 23.5.0\n",
      "  latest version: 24.1.2\n",
      "\n",
      "Please update conda by running\n",
      "\n",
      "    $ conda update -n base -c defaults conda\n",
      "\n",
      "Or to minimize the number of packages updated during conda update use\n",
      "\n",
      "     conda install conda=24.1.2\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!conda install numpy matplotlib \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9b278fb-005f-4c9e-b4e7-19dec33b3a5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "t = np.linspace(0, 2*np.pi, 100)\n",
    "x = 16 * np.sin(t)**3\n",
    "y = 13 * np.cos(t) - 5 * np.cos(2*t) - 2 * np.cos(3*t) - np.cos(4*t)\n",
    "\n",
    "plt.plot(x, y, 'r')\n",
    "plt.axis('equal')\n",
    "plt.title('Heart Shape')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44f701da-bd28-4632-bfd6-49f41c4a8f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 生成随机数\n",
    "x = np.arange(0, 10)\n",
    "y = np.random.randint(0, 100, size=10)\n",
    "\n",
    "# 绘制折线图\n",
    "plt.plot(x, y, marker='o', linestyle='-')\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('Random Line Plot')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dac59d71-2e5b-460c-9c52-7a9adf242410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting seaborn\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/83/11/00d3c3dfc25ad54e731d91449895a79e4bf2384dc3ac01809010ba88f6d5/seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
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      "Collecting pandas>=1.2 (from seaborn)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/93/26/2a695303a4a3194014dca7cb5d5ce08f0d2c6baa344fb5f562c642e77b2b/pandas-2.2.1-cp310-cp310-win_amd64.whl (11.6 MB)\n",
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      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.0)\n",
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      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.0.9)\n",
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      "Collecting tzdata>=2022.7 (from pandas>=1.2->seaborn)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl (345 kB)\n",
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      "Requirement already satisfied: six>=1.5 in c:\\users\\wdcpclover\\miniconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "Installing collected packages: tzdata, pandas, seaborn\n",
      "Successfully installed pandas-2.2.1 seaborn-0.13.2 tzdata-2024.1\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a97ee93-b153-4c96-9865-a36c2a5a8092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 生成示例数据\n",
    "np.random.seed(0)\n",
    "x = np.random.rand(100)\n",
    "y = 2 * x + np.random.normal(0, 0.1, 100)\n",
    "\n",
    "# 使用Seaborn绘制线性回归模型的散点图和拟合线\n",
    "sns.regplot(x=x, y=y)\n",
    "\n",
    "plt.title('Seaborn Linear Relationship Example')\n",
    "plt.xlabel('X Value')\n",
    "plt.ylabel('Y Value')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "936809a9-dfa8-40da-9815-3d7cac68dfb8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
